Segmented hash

Hash tables provide efficient table implementations, achieving O(1), query, insert and delete operations at low loads. However, at moderate or high loads collisions are quite frequent, resulting in decreased performance. In this paper, we propose the segmented hash table architecture, which ensures constant time hash operations at high loads with high probability. To achieve this, the hash memory is divided into N logical segments so that each incoming key has N potential storage locations; the destination segment is chosen so as to minimize collisions. In this way, collisions, and the associated probe sequences, are dramatically reduced. In order to keep memory utilization minimized, probabilistic filters are kept on-chip to allow the N segments to be accessed without increasing the number of off-chip memory operations. These filters are kept small and accurate with the help of a novel algorithm, called selective filter insertion, which keeps the segments balanced while minimizing false positive rates (i.e., incorrect filter predictions). The performance of our scheme is quantified via analytical modeling and software simulations. Moreover, we discuss efficient implementations that are easily realizable in modern device technologies. The performance benefits are significant: average search cost is reduced by 40% or more, while the likelihood of requiring more than one memory operation per search is reduced by several orders of magnitude.